This invention relates to segmentation of medical images. More particularly, the invention relates to a method and system for segmenting an object of interest in three-dimensional medical images for use in volumetric measurement.
It is well-known to obtain three-dimensional (3D) arrays of data representing one or more physical properties within an interior of a solid body, for example, anatomical structures. In medical imaging, such data is obtained by a variety of non-invasive methods such as computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), x-ray or a combination thereof. Regardless of the image data acquisition method, the 3D array of data typically consists of a plurality of sets of three-dimensional coordinates distributed at regular positions about the body of interest. There are a variety of techniques available to generate a three-dimensional model or structure. Typically, a seed voxel (volume element) is placed within the anatomical structure of interest and adjacent voxels are successively analyzed and identified as belonging to the same structure generally if they are adjacent to a previously identified voxel and they meet a specified attribute, such as intensity or radiological density. In accordance with any of the known techniques, a 3D image is obtained for visualization.
The three-dimensional (3D) visualization of internal anatomical structures is a known and particularly useful technique for medical professionals and research scientists. Three-dimensional models enable the ability to rotate the model or virtual representation of the anatomical structure, as well as adjust a point of perspective and zoom in/out from features of interest. Additionally, volumetric measurements are enabled by a variety of known 3D image processing techniques.
Three-dimensional visualization and volume measurement is of particular interest for studying degenerative brain diseases such as Alzheimer's disease (AD). There are 4 million people in the United States diagnosed with dementia in Alzheimer's disease. Examination of the Alzheimer brain pathology shows extensive β-amyloid plaque, neuron tangles and brain atrophy. Typically, magnetic resonance imaging brain volume measurements are used to monitor the disease progression. Normal aging brain atrophy is only about a 3.5% decrease per decade, but the rate of atrophy increases in subjects exhibiting dementia. Thus, brain volume measurements provide a measurable correlation available to predict Alzheimer's disease.
Measurements of brain volume from 3D magnetic resonance images either by registration methods or by segmentation methods are typically tedious because manual editing is required to remove the scalp from the intracranial volume in a 3D representation. Supervised segmentation methods are not sufficiently accurate because of inter observer error. Another technique, known as active contours, has been able to segment the brain using a model where the surface of the active contour (bubble) moves at a velocity that depends on curvature and diffusive flow. This involves growing a bubble constrained by image parameters such as gradients and curvature and constructing a force that stops the bubble growth. However, most of the available techniques encounter some degree of error. For example, the connected volume after segmentation may include regions that are not of interest thus requiring some user intervention. Further, the connected volume may include connection through a undesired narrow region, bridge or other small structure that connects different regions that are desirably separated.
What is needed is a method and system for segmenting three-dimensional medical images in an automatic manner with minimal user intervention.